@article {2019HRI, title = {Learning the Correct Robot Trajectory in Real-Time from Physical Human Interactions}, journal = {ACM Transactions on Human-Robot Interaction (THRI)}, volume = {9}, number = {1}, year = {2019}, pages = {1}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_ACMTHRI_2019.pdf}, author = {Losey, Dylan P and O{\textquoteright}Malley, Marcia K} } @proceedings {1862, title = {Learning from Physical Human Corrections, One Feature at a Time}, year = {2018}, month = {03/2018}, publisher = {ACM/IEEE}, address = {Chicago, USA}, abstract = {

We focus on learning robot objective functions from human guidance: specifically, from physical corrections provided by the person while the robot is acting. Objective functions are typically parametrized in terms of features, which capture aspects of the task that might be important. When the person intervenes to correct the robot{\textquoteright}s behavior, the robot should update its understanding of which features matter, how much, and in what way. Unfortunately, real users do not provide optimal corrections that isolate exactly what the robot was doing wrong. Thus, when receiving a correction, it is difficult for the robot to determine which features the person meant to correct, and which features were changed unintentionally. In this paper, we propose to improve the efficiency of robot learning during physical interactions by reducing unintended learning. Our approach allows the human-robot team to focus on learning one feature at a time, unlike state-of-the-art techniques that update all features at once. We derive an online method for identifying the single feature which the human is trying to change during physical interaction, and experimentally compare this one-at-a-time approach to the all-at-once baseline in a user study. Our results suggest that users teaching one-at-a-time perform better, especially in tasks that require changing multiple features.

}, doi = {10.1145/3171221.3171267}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_HRI2018.pdf}, author = {Andrea Bajcsy and Dylan P. Losey and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @proceedings {1830, title = {Learning Robot Objectives from Physical Human Interaction}, year = {2017}, month = {11/2017}, pages = {217-226}, publisher = {PMLR}, address = {Mountain View, CA}, abstract = {

When humans and robots work in close proximity, physical interaction is inevitable. Traditionally, robots treat physical interaction as a disturbance, and resume their original behavior after the interaction ends. In contrast, we argue that physical human interaction is informative: it is useful information about how the robot should be doing its task. We formalize learning from such interactions as a dynamical system in which the task objective has parameters that are part of the hidden state, and physical human interactions are observations about these parameters. We derive an online approximation of the robot{\textquoteright}s optimal policy in this system, and test it in a user study. The results suggest that learning from physical interaction leads to better robot task performance with less human effort.

}, keywords = {learning from demonstration, physical human-robot interaction}, url = {http://proceedings.mlr.press/v78/bajcsy17a.html}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/CoRL_2017.pdf}, author = {Andrea Bajcsy and Dylan P. Losey and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @proceedings {1766, title = {Leveraging disturbance observer based torque control for improved impedance rendering with series elastic actuators}, year = {2015}, month = {09/2015}, pages = {1646-1651}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Mehling2015\%20-\%20Leveraging\%20DOB\%20SEAs.pdf}, author = {Mehling, J.S. and James Holley and O{\textquoteright}Malley, M.K.} } @proceedings {chawda2011, title = {A Lyapunov Approach for SOSM Based Velocity Estimation and its Application to Improve Bilateral Teleoperation Performance}, year = {2011}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/1322-DSCC2011-6181.pdf}, author = {Vinay Chawda and Marcia K. O{\textquoteright}Malley} } @proceedings {912, title = {Long-term double integration of acceleration for position sensing and frequency domain system identification}, year = {2010}, address = {Montr{\'e}al, Canada}, abstract = {

We present results from successful implementation of long-term (\>10 seconds) real-time integration of acceleration to measure position. We evaluated two analog circuit designs for double integration of an acceleration signal. Our circuit design features three high-pass filters and two first order integrators, leading to a critically damped double integrator. The second design we have implemented is a second order underdamped integrator reported in the literature. Accuracy of both circuits in sensing position based on only accelerometer readings was experimentally evaluated by comparison with encoder readings. We conclude that a critically damped double integrator coupled with an accelerometer is well-suited for frequency domain system identification, thanks to reliable position measurement capability with minimal interference to system dynamics.

}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/912-Gilbert2010AIM.pdf}, author = {Gilbert, Hunter B and Ozkan Celik and O{\textquoteright}Malley, M.K.} } @proceedings {108, title = {A Low Cost Vibrotactile Array to Manage Respiratory Motion}, year = {2009}, month = {03/2009}, publisher = {IEEE}, address = {Salt Lake city, Utah}, abstract = {

We present a tactile Respiratory Management System (tRMS) to manage and control breathing patterns of cancer patients undergoing radiation therapy. The system comprises of an array of small vibrating motors and a control box that supplies power to and provides a control interface for up to twelve motors through the parallel port of a standard personal computer. The vibrotactile array can be fastened along the forearm, arm, thigh, leg or abdomen in any configuration using Velcro and fabric wraps. All motors are operated in a binary fashion, i.e. on or off, with quick response time and perceivable vibration magnitudes. The tRMS system is inexpensive and portable, providing spatiotemporal variations in tactile cues to regulate respiratory motion during radiotherapy. The system will also be used in future psychophysical studies to determine effective use of tactile cues to control human motor actions.

}, keywords = {Tactile feedback, vibrotactile array}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/108-0236.pdf}, author = {Ali Israr and Dillon P. Eng and Sastry S. Vedam and O{\textquoteright}Malley, M.K.} }